Victor Hesselbrock

Bio: Victor Hesselbrock is an academic researcher from University of Connecticut. The author has contributed to research in topics: Alcohol dependence & Poison control. The author has an hindex of 82, co-authored 365 publications receiving 25263 citations. Previous affiliations of Victor Hesselbrock include University of Iowa Hospitals and Clinics & Veterans Health Administration.

A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA.

Abstract: Within- and cross-center test-retest studies were conducted to study the reliability of a new, semistructured, comprehensive, polydiagnostic psychiatric interview being used in a multisite genetic linkage study of alcoholism. Findings from both studies indicated that reliability for the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) was high for DSM-III-R substance dependence disorders, but less so for substance abuse disorders. Reliability of depression was good in both studies, but mixed for antisocial personality disorder (ASP). Findings are presented in terms of specific substance dependence and abuse diagnoses, as well as for depression and ASP. Criterion-specific reliabilities are examined by type of substance used. Although SSAGA was designed to provide for broad phenotyping of alcoholism, review of its new features suggests its suitability for a variety of family studies, not just those focusing on substance abuse.

Types of alcoholics, I. Evidence for an empirically derived typology based on indicators of vulnerability and severity.

Abstract: • An empirical clustering technique was applied to data obtained from 321 male and female alcoholics to identify homogeneous subtypes having discriminative and predictive validity. The clustering solution identified two "types" of alcoholics who differed consistently across 17 defining characteristics in the male and female samples. One group, designated type A alcoholics, is characterized by later onset, fewer childhood risk factors, less severe dependence, fewer alcohol-related problems, and less psychopathological dysfunction. The other group, termed type B alcoholics, is characterized by childhood risk factors, familial alcoholism, early onset of alcohol-related problems, greater severity of dependence, polydrug use, a more chronic treatment history (despite their younger age), greater psychopathological dysfunction, and more life stress. The two types also differed with respect to treatment outcome assessed prospectively at 12 and 36 months. The results are consistent with historical and contemporary typological theories that have postulated similar subgroups of alcoholics. The findings suggest that an empirically derived, multivariate typology of alcoholism has theoretical implications for explaining the heterogeneity among alcoholics and may provide a useful basis for predicting course and estimating treatment response.

Genome‐wide search for genes affecting the risk for alcohol dependence

Abstract: Alcohol dependence is a leading cause of morbidity and premature death. Several lines of evidence suggest a substantial genetic component to the risk for alcoholism: sibs of alcoholic probands have a 3-8 fold increased risk of also developing alcoholism, and twin heritability estimates of 50-60% are reported by contemporary studies of twins. We report on the results of a six-center collaborative study to identify susceptibility loci for alcohol dependence. A genome-wide screen examined 291 markers in 987 individuals from 105 families. Two-point and multipoint nonparametric linkage analyses were performed to detect susceptibility loci for alcohol dependence. Multipoint methods provided the strongest suggestions of linkage with susceptibility loci for alcohol dependence on chromosomes 1 and 7, and more modest evidence for a locus on chromosome 2. In addition, there was suggestive evidence for a protective locus on chromosome 4 near the alcohol dehydrogenase genes, for which protective effects have been reported in Asian populations.

A validity study of the SSAGA- a comparison with the SCAN

Abstract: Objective. This study examined the concurrent diagnostic validity of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) across alcohol and drug dependencies, major depression, anxiety disorders and ASPD. The Schedule for Clinical Assessment in Neuropsychiatry (SCAN) was selected as the comparison instrument because it arises from a different tradition and uses a different format for its administration. The SCAN has been shown to be valid and applicable cross-culturally. Method. Subjects included 38 men and 42 women volunteers from another study and from an outpatient psychiatry clinic. Selected sections of both the SSAGA and the SCAN interviews were administered to all subjects, approximately 1 week apart, in a randomized order. Because the SCAN does not assess Antisocial Personality Disorder (ASPD), the ASPD section of the Structured Clinical Interview for DSM-III-R (SCID) was substituted for this comparison. Results. The Kappa statistic was used to measure concordance between the two instruments. Kappa for alcohol dependence was in the acceptable range (0.63). Kappas were lower for sedative dependence (0.48) and for cannabis dependence (0.53), but higher for cocaine and stimulant dependence (0.85) and for opioid dependence (0.73). Kappa for major depression and the ASPD diagnoses were high (0.71 and 0.70), but slightly lower agreement was found for panic disorder (0.62). Kappa for social phobia was 0.47. Conclusion. These data, combined with results from two previous studies which examined reliability, indicate that the SSAGA is a highly reliable and valid instrument for use in studies of a variety of psychiatric disorders, including alcohol and drug dependence.

Variations in GABRA2, Encoding the α2 Subunit of the GABAA Receptor, Are Associated with Alcohol Dependence and with Brain Oscillations

Abstract: Alcoholism is a complex disease with both genetic and environmental risk factors. To identify genes that affect the risk for alcoholism, we systematically ascertained and carefully assessed individuals in families with multiple alcoholics. Linkage and association analyses suggested that a region of chromosome 4p contained genes affecting a quantitative endophenotype, brain oscillations in the beta frequency range (13–28 Hz), and the risk for alcoholism. To identify the individual genes that affect these phenotypes, we performed linkage disequilibrium analyses of 69 single-nucleotide polymorphism (SNPs) within a cluster of four GABAA receptor genes, GABRG1, GABRA2, GABRA4, and GABRB1, at the center of the linked region. GABAA receptors mediate important effects of alcohol and also modulate beta frequencies. Thirty-one SNPs in GABRA2, but only 1 of the 20 SNPs in the flanking genes, showed significant association with alcoholism. Twenty-five of the GABRA2 SNPs, but only one of the SNPs in the flanking genes, were associated with the brain oscillations in the beta frequency. The region of strongest association with alcohol dependence extended from intron 3 past the 3′ end of GABRA2; all 43 of the consecutive three-SNP haplotypes in this region of GABRA2 were highly significant. A three-SNP haplotype was associated with alcoholism, with P=.000000022. No coding differences were found between the high-risk and low-risk haplotypes, suggesting that the effect is mediated through gene regulation. The very strong association of GABRA2 with both alcohol dependence and the beta frequency of the electroencephalogram, combined with biological evidence for a role of this gene in both phenotypes, suggest that GABRA2 might influence susceptibility to alcohol dependence by modulating the level of neural excitation.

Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

Abstract: Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test ...

Harrison's Principles of Internal Medicine

Abstract: The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.